Sponsored by
Vmake Labs Video Enhancer
-Vmake Labs Video Enhancer uses AI to improve video quality, restore details, reduce blur, and upscale videos to 8K.
NormalCrafter generates
temporally consistent surface normal maps
from video using a Stable Video Diffusion (SVD) backbone fine-tuned for normal estimation. Unlike image-based methods (e.g., Marigold), NormalCrafter operates natively on video sequences, producing smooth frame-to-frame normals without flickering.
Key Features
Video-native
: Processes temporal sequences for coherent normals across frames
SVD backbone
: Built on
stabilityai/stable-video-diffusion-img2vid-xt
High resolution
: Supports up to 1024px inference
Apache-2.0 Licensed
: Free for commercial and personal use
Model Files
File
Size
Description
unet/diffusion_pytorch_model.safetensors
3.05 GB
Fine-tuned UNet for normal estimation
image_encoder/model.fp16.safetensors
1.26 GB
CLIP image encoder (fp16)
vae/diffusion_pytorch_model.safetensors
196 MB
VAE decoder
Usage in ComfyUI-FFMPEGA
NormalCrafter is available as:
Standalone skill
:
normalcrafter
in the FFMPEGA agent
No-LLM mode
: Select
normalcrafter
in the agent node dropdown
AI Relighting
: Enable "Use NormalCrafter" in the Video Editor's Relight panel for physically-based relighting
Citation
@article{normalcrafter2024,
title={NormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors},
author={Yanrui Bin and Wenbo Hu and Haoyuan Wang and Xinya Chen and Bing Wang},
year={2024}
}
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NormalCrafter is an open source model from GitHub that offers a free installation service, and any user can find NormalCrafter on GitHub to install. At the same time, huggingface.co provides the effect of NormalCrafter install, users can directly use NormalCrafter installed effect in huggingface.co for debugging and trial. It also supports api for free installation.